Fine Sensorless Force Control Realization Based on Dither Periodic Component Elimination Kalman Filter and Wide Band Disturbance Observer

Phuong, Thao Tran; Ohishi, Kiyoshi; Yokokura, Yuki

doi:10.1109/tie.2018.2883256

Cited by 33 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[31] proposed a force/position decentralized robust controller, taking into account a constrained reconfigurable robot. [32] formulated a dither periodic component elimi-nation Kalman filter, enhanced by a disturbance observer. By means of the developed framework, it is possible to perform the estimation of the interaction force, building a high accuracy sensorless force-tracking controller capable to compensate for static friction effects.…”

Section: ) Sensorless Force Controlmentioning

confidence: 99%

“…1) Inner joint position control the diagonal elements of K p,c has been imposed equal to 0.25 1/s; K p = diag([1000, 1500, 1500, 1500, 400, 200, 150]) Nm/rad has been imposed; K d = diag( [32,40,30,45,20,20,10]) Nms/rad has been imposed; K i = diag( [15,15,20,40,40,30,40]) Nm/(s rad) has been imposed; friction compensation as in [49] have been implemented. K p , K d , and K i have been optimized as in [41].…”

Section: B Control Parameters Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Sensorless Optimal Switching Impact/Force Controller

et al. 2021

View full text Add to dashboard Cite

Intelligent interaction control is required in many fields of application, in which different operative situations have to be faced with different controllers. Being able to switch between optimized controllers is, indeed, of extreme importance to maximize the task performance in the different operative conditions (i.e., free-space motion and contact), especially when considering sensorless robots. To deal with the proposed context, a sensorless optimal switching impact/force (OSIF) controller is proposed. The low-level robot control is composed of an inner joint position controller, fed by an outer Cartesian impedance controller with a reference position. The estimation of the external wrench is implemented by means of an Extended Kalman Filter (EKF). The high-level controller (feeding the Cartesian impedance controller with the setpoint) is composed of an optimized impact controller (LQR-based controller), an optimized force controller (SDREbased controller), and a continuous switching mechanism (Fuzzy Logic-based). In addition, the output of the switching mechanism is used to adapt the Cartesian impedance control parameters (i.e., stiffness and damping parameters). Experimental tests have been performed on a Franka EMIKA panda robot to validate the proposed controller. Obtained results show the capabilities of the OSIF controller, being able to detect task phase transitions while satisfying the target performance.

show abstract

Section: ) Sensorless Force Controlmentioning

confidence: 99%

Section: B Control Parameters Settingsmentioning

confidence: 99%

Sensorless Optimal Switching Impact/Force Controller

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In Zhou et al (2017) a force/position decentralized robust control problem for constrained reconfigurable manipulator system with parameter perturbation and unmodeled dynamics is presented. In Phuong et al (2018) a force estimation approach based on a dither periodic component elimination Kalman filter and on a disturbance observer for realization of a fine sensorless force control system under the existence of static friction is proposed. In Nakamura et al (2018) a notch-type friction free disturbance observer is used for sensorless force control.…”

Section: Related Workmentioning

confidence: 99%

Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks

Roveda

Piga

2020

Int J Intell Robot Appl

View full text Add to dashboard Cite

Industrial robots are increasingly used in highly flexible interaction tasks, where the intrinsic variability makes difficult to pre-program the manipulator for all the different scenarios. In such applications, interaction environments are commonly (partially) unknown to the robot, requiring the implemented controllers to take in charge for the stability of the interaction. While standard controllers are sensor-based, there is a growing need to make sensorless robots (i.e., most of the commercial robots are not equipped with force/torque sensors) able to sense the environment, properly reacting to the established interaction. This paper proposes a new methodology to sensorless force control manipulators. On the basis of sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is designed to estimate the interaction exchanged between the robot and the environment. Such an estimation is then used in order to close a robust high-performance force loop, designed exploiting a variable impedance control and a State Dependent Riccati Equation (SDRE) force controller. The described approach has been validated in simulations. A Franka EMIKA panda robot has been considered as a test platform. A probing task involving different materials (i.e., with different stiffness properties) has been considered to show the capabilities of the developed EKF (able to converge with limited errors) and controller (preserving stability and avoiding overshoots). The proposed controller has been compared with an LQR controller to show its improved performance.

show abstract

“…A Kalman filtered DOB (KF-DOB) [14], [15]; specifically, Kalman filtered MDOB, and disturbance KF (DKF) [16], designed using an internal model augmented by a disturbance model, have been proposed to solve these trade-offs. The KF is a method of estimating the state of the plants using the minimization of the mean square error, with the capability to process influences of white noise.…”

Section: Introductionmentioning

confidence: 99%

Variable Forgetting Factor-Based Adaptive Kalman Filter With Disturbance Estimation Considering Observation Noise Reduction

2021

View full text Add to dashboard Cite

This paper addresses the influence reduction of quantization and observation noises in a disturbance observer (DOB) technique. DOB is a disturbance estimation method that makes control systems robust. However, in implementing low-resolution sensors, disturbance estimates from DOB are considerably influenced by observation and quantization noises. In this paper, a novel DOB design method for simultaneous estimation of state and unknown disturbances, including the reduction of noise influences, is proposed. The proposed method is divided into two components. The first component is a Kalman filter (KF)-based DOB for simultaneous estimation of state and unknown disturbances. To improve the estimation performance through the KF-based DOB, a forgetting factor-based adaptive KF (FAKF) was employed. The second component is an adaptive law for the forgetting factor in the FAKF. The adaptive law is used for balancing the estimation accuracy and observation noise reduction. Simulation results involving various types of noise environments demonstrate the effectiveness of the proposed method.

show abstract

Fine Sensorless Force Control Realization Based on Dither Periodic Component Elimination Kalman Filter and Wide Band Disturbance Observer

Cited by 33 publications

References 29 publications

Sensorless Optimal Switching Impact/Force Controller

Sensorless Optimal Switching Impact/Force Controller

Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks

Variable Forgetting Factor-Based Adaptive Kalman Filter With Disturbance Estimation Considering Observation Noise Reduction

Contact Info

Product

Resources

About